The benefits of visualization have been discussed widely and it is already implemented into library services. However, use cases for visualization have been mostly focused on collection analysis to improve collection development policies and budget management, not for discovery services that take full advantage of the rich information contained in library catalog records. One of the challenges of working with library catalog records for visualization is the sheer volume of elements (such as control field, data field, subfield, and indicators) and information included in the MAchine-Readable Cataloging (MARC) format records. As is well-known, there are more than 1,900 fields in the MARC 21, which is just too many to use for effective visualizations (Moen and Benardino, 2003). In addition, some fields are used for recording the same information, for example, the control field 008 positions 7 to 14 and the subfield $c of the data field 264 are used for the production related date information. Instead of showing a clear relationship between resources, the large number of elements and duplicated information included in the catalog record may muddle those relationships in any visualization. The question then is which information added in which fields of the MARC 21 format catalog records should be considered essential information to be included in library catalog data visualizations for discovery. This paper explores ways to improve discovery service by visualizing selective library data.